by Camille Chin | March 30, 2020
Even if your Excel skills are through the roof, flat business data on hundreds of spreadsheets won’t serve you in 2020. To stay competitive, businesses need to augment their real-time, baseline data with artificial intelligence (AI) technology.
AI isn’t a new concept — the term itself was coined in 1956 — but the advent of cloud-based services and the ever-evolving power of computing have facilitated the ways AI can be used by retailers. AI is already being used for hyper personalization, identifying patterns and anomalies, and predicting behavior and failures. Improved accuracy, speed and responsiveness are additional reasons why 60% of marketers aim to increase their use of AI in the next 12 months.
Business insights like these are invaluable, but it’s also imperative that they are easy to understand. Because our culture is largely visual, data visualization is essential to exploring the trillions of rows of business insights generated daily. In addition to leveraging our natural human ability to quickly see patterns, charts, graphs and maps with colors and shapes make insights easily apparent to ordinary people; when you can see insights, you can internalize them.
AI learning algorithms in combination with data visualizations deliver complex connections between customers, stores, products and seasons, but AI has the potential to reinvent retail journeys in other ways too. Change is already happening; find out how below.
H&M: More Relevant Products in Each and Every Store
H&M has close to 5000 stores worldwide and for years all of their global locations had very similar merchandise. The fast-fashion retailer reported a 14 percent profit loss in 2017 and had to slash prices to clear a whopping US$4 billion of unsold inventory.
Today, H&M relies on AI to analyze store receipts, returns, blog performance and more to effectively customize the merchandise mix of individual stores. When the Swedish retailer began experimenting with AI in 2018, it was discovered, for example, that floral skirts in pastel colors sold at better-than-predicted rates at the H&M in Stockholm’s Östermalm neighborhood.
Adapting H&M’s merchandise mix hasn’t been easy: the retailer receives five billion in-store and online visits a year, and processes 800 million transactions. To help, the retailer has bulked up their resources too: they employed 200 data scientists, analysts and engineers.
West Elm: Connecting Customers with Products — Fast
According to Gartner research, brands that redesign their websites to enable visual and voice queries by 2021 will increase their digital commerce revenue by 30 percent. Gen Z and Millennials shop via visual, mobile searches and 60 percent of them say visual searches are a priority.
Enter West Elm. The furniture retailer launched the West Elm Pinterest Style Finder a few years ago. (Pinterest cleared US$1 billion in revenue in 2019; US$1.5 billion is expected in 2020).
West Elm’s online catalogue consists of 5000 items, 90 percent of which are exclusive and designed in house. To help shoppers find the type of armchair or nightstand or pouf they want in just seconds, the online tool uses neural networks (algorithms modeled after the human brain) to narrow searches based images that shoppers have saved on their Pinterest boards. The experience is akin to showing an in-store associate a picture; the Style Finder automates, accelerates and simplifies the process. Merchandising teams have also used the tool to find holes in assortment offerings.
Walmart: Automating In-Store Truths
Walmart operates more than 5000 stores in the US and soon 20 percent of them will have robots. The autonomous machines made by Pittsburgh start up Bossa Nova Robotics are already in 350 stores; in January 2020 it was announced that an additional 650 stores will get the six-foot-tall, armless machines later this year.
The custom robots feature 3D cameras capable of scanning shelves for item availability, location and price verification. The robots automate inventory data collection and analysis — a complex and time consuming task in stores as large as Walmart. The robots aren’t designed to replace manual restocking; the goal, rather, is to free staff of dull and repetitive tasks so they can more effectively spend their time fixing problems and connecting with shoppers.
Analytics, AI and You
Automating and accelerating data analysis, product searches and inventory reviews are just some of the ways AI is benefiting retailers of all sizes and verticals today. AI may appear in your stores in the changing rooms, on product shelves or at checkout counters, but it will inevitably be present in the very near future — robots or not.
Jesta’s AI-driven Vision Analytics product is powered by Snowflake and Tableau. To get help launching your digital transformation, contact us or request a demo.
